The Essentials of Biostatistics for Physicians, Nurses, and Clinicians , by M. R. Chernick

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  • This article was downloaded by: [Osaka University]On: 01 December 2014, At: 22:58Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

    Journal of Biopharmaceutical StatisticsPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/lbps20

    The Essentials of Biostatistics for Physicians, Nurses,and Clinicians, by M. R. ChernickSteve SimonPublished online: 18 Jan 2013.

    To cite this article: Steve Simon (2013) The Essentials of Biostatistics for Physicians, Nurses, and Clinicians, by M. R.Chernick, Journal of Biopharmaceutical Statistics, 23:1, 272-274, DOI: 10.1080/10543406.2013.737219

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  • Journal of Biopharmaceutical Statistics, 23: 272274, 2013Copyright Taylor & Francis Group, LLCISSN: 1054-3406 print/1520-5711 onlineDOI: 10.1080/10543406.2013.737219

    BOOK REVIEW

    The Essentials of Biostatistics for Physicians, Nurses, and Clinicians, by M. R.Chernick, Hoboken, NJ: Wiley, 2011, ISBN 978-0-470-64185-9, x + 214 pp., $74.95.

    If youve ever browsed through the Amazon web site for statistics books,youve probably seen a review by Michael Chernick. Hes commented on well over600 books on Amazon, with the bulk of them being books for advanced statisticsapplications. Dr. Chernicks reviews are always detailed and informative. I wasreally hoping that I would enjoy this book as much as I have enjoyed those Amazonreviews, but I was sadly disappointed.

    As described in the preface, this book evolved from a series of PowerPointlectures given to nurses and doctors (including residents and fellows) at theLankenau Institute. Ive done similar lectures myself, and this is a challenging butrewarding group to teach. Theyve already had at least one or two statistics coursesin their training, but they often find out on the job how important statistics reallyis. So they are very receptive to the subject and can visualize real-world applicationsfar better than they could have as a student.

    You have some interesting options for this audience. Theyve already seenthis material as students and even if they say they dont remember anything fromtheir classes, they pick up the material more quickly the second time. You have theoption of using a radically different teaching order, because youre trying to refreshmemories rather than build from foundational concepts to more advanced topics.You can also include advanced topics in earlier lectures because they have at leasta passing familiarity with these topics already.

    In spite of the possibilities opened by this additional flexibility, this bookpresents the topics in the same order as pretty much most of the other books outthere. After a motivational introduction, the book covers sampling concepts, simplegraphs and descriptive statistics, the normal, binomial, and Poisson distributions,simple confidence intervals and hypothesis tests, correlation, linear, and logisticregression, two-by-two and larger contingency tables, nonparametric methods, andfinishes with a chapter on survival analysis. This last chapter is one of the best in thebook, as it succinctly covers life tables, the KaplanMeier curve, parametric survivalmodels, and the Cox proportional hazards regression model.

    Very brief discussions of diagnostic testing (sensitivity, specificity, and theROC [receiver operating characteristic] curve) and meta-analysis are tucked into thechapter on hypothesis testing. These seem out of place in this chapter, but there isno obvious alternative location for these topics.

    One unusual and welcome addition is the description of the bootstrap. This isnot an easy topic to present, but Dr. Chernick provides enough details so that the

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    reader can understand the fundamental concepts. Theres also a brief but importantdiscussion of superiority, equivalence, and noninferiority hypotheses.

    A book like this can take several different approaches and no one approach isgoing to please everyone. This book emphasizes a conceptual understanding of thetopics and hand calculation from algebraic formulas. Dr. Chernick largely ignoresoutput from statistical packages, though an SAS program and output in the chapteron regression is a notable exception. Id prefer a book with fewer formulas andmore computer output, but there is nothing inherently wrong with Dr. Chernicksapproach.

    There are sample exercises at the end of each chapter. These are well doneand use realistic examples that doctors and nurses can relate to. Answers to someexercises appear at the end of the book.

    The bibliography for this book is brief (80 references total), but most readersof this book are unlikely to probe too deeply beyond the material in the text itself.The references are very current, with a median year of publication of 2000 and a75th percentile of 2005.

    There are a lot of small annoyances in the book. Dr. Chernick claims onpage 16 that bias can be avoided by randomization. Clearly, randomization preventssome types of bias, but other biases, such as those introduced by unblindedevaluations, will be present even in randomized studies. Its a small thing, and couldhave been easily fixed by adding a qualifier like some or most or explainingwhich type of bias is avoided through randomization. Thats a problem throughoutthe book: small errors that could have been easily corrected.

    On page 12, Dr. Chernick claims that case-control studies can sometimesbe prospective. Since cases and controls are selected on the basis of the outcomemeasure and since the outcome is not known at the start of a prospective study, theonly way you could have a prospective case-control study is if you invented a timemachine first.

    On the same page, he defines a prospective study as one that is planned in thepresent and takes place in the future. This at first seems like a clever way to defineprospective, but if you think long enough about this, youd realized that every studyis planned in the present and takes place in the future.

    The section on meta-analysis focuses exclusively on the Fisher method ofcombining p values. This is a method that is elegant and historically interesting. Butit also has serious limitations (Wolf, 1986, p. 19) and is rarely used in practice.

    Many of the graphs in this book have problems. Bar charts of probabilities forthe binomial and Poisson distribution on page 60 start at the value of 1 rather than0, so the label on the axis reads number of successes +1. A power curve on page74 has a range for the y-axis that goes from 0 to 1.2, even though it is impossible tohave a power greater than 1.0. A graph illustrating regression residuals on page 101includes some crudely pasted on graphics that align poorly with the original figure.

    Not all the graphs are this bad. A survival curve on page 159 is simple andelegant. In contrast to the graphs, all the tables are laid out cleanly and clearly.

    Its great to have a basic statistics book targeted to nurses and doctors,but this book has too many small problems. I hate to nitpick because I knowfrom my own experience how easy it is to let embarrassing errors slip into print.Unfortunately, the cumulative effect of all these small annoyances does add up to a

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    serious problem. These could easily be fixed in a second edition, so I hold out hopethat this could become an excellent resource.

    Steve SimonP. Mean ConsultingLeawood, Kansas

    REFERENCE

    Wolf, F. M. (1986). Meta-Analysis: Quantitative Methods for Research Synthesis. NewburyPark, CA: Sage.

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